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Conformal Prediction

Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.

Papers

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Showing 651–675 of 704 papers

TitleStatusHype
Label Noise Robustness of Conformal Prediction—0
SAFE: Multitask Failure Detection for Vision-Language-Action Models—0
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems—0
Conformal Prediction Regions are Imprecise Highest Density Regions—0
SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation—0
Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers—0
Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction—0
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression—0
Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction—0
Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores—0
Weighted Aggregation of Conformity Scores for Classification—0
Conformal Predictions for Longitudinal Data—0
Conformal Predictions under Markovian Data—0
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets—0
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift—0
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Sample-Efficient Safety Assurances using Conformal Prediction—0
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees—0
Conformal Prediction with Learned Features—0
Closing the Loop on Runtime Monitors with Fallback-Safe MPC—0
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models—0
Conformal Prediction with Temporal Quantile Adjustments—0
Conformal Prediction with Upper and Lower Bound Models—0
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